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Your search keyword '"Lu, Chun-Feng"' showing total 25 results
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25 results on '"Lu, Chun-Feng"'

4. A late eating midpoint is associated with increased risk of diabetic kidney disease: a cross-sectional study based on NHANES 2013–2020.

Author

Lu, Chun-feng, Cang, Xiao-min, Liu, Wang-shu, Wang, Li-hua, Huang, Hai-yan, Wang, Xue-qin, Zhao, Li-hua, and Xu, Feng

Abstract

Background: Modifying diet is crucial for diabetes and complication management. Numerous studies have shown that adjusting eating habits to align with the circadian rhythm may positively affect metabolic health. However, eating midpoint, eating duration, and their associations with diabetic kidney disease (DKD) are poorly understood. Methods: The National Health and Nutrition Examination Survey (2013–2020) was examined for information on diabetes and dietary habits. From the beginning and ending times of each meal, we calculated the eating midpoint and eating duration. Urinary albumin-to-creatinine ratio (UACR) ≥ 30 mg/g and/or estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 were the specific diagnostic criteria for DKD. Results: In total, details of 2194 subjects with diabetes were collected for analysis. The overall population were divided into four subgroups based on the eating midpoint quartiles. The prevalence of DKD varied noticeably (P = 0.037) across the four categories. When comparing subjects in the second and fourth quartiles of eating midpoint to those in the first one, the odds ratios (ORs) of DKD were 1.31 (95% CI, 1.03 to 1.67) and 1.33 (95% CI, 1.05 to 1.70), respectively. And after controlling for potential confounders, the corresponding ORs of DKD in the second and fourth quartiles were 1.42 (95% CI, 1.07 to 1.90) and 1.39 (95% CI, 1.04 to 1.85), respectively. Conclusions: A strong correlation was found between an earlier eating midpoint and a reduced incidence of DKD. Eating early in the day may potentially improve renal outcomes in patients with diabetes. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Additional file 1 of Association of sleep quality with glycemic variability assessed by flash glucose monitoring in patients with type 2 diabetes

Author

Yang, Yang, Zhao, Li-hua, Li, Dan-dan, Xu, Feng, Wang, Xiao-hua, Lu, Chun-feng, Wang, Chun-hua, Yu, Chao, Zhang, Xiu-lin, Ning, Li-yan, Wang, Xue-qin, Su, Jian-bin, and Wang, Li-hua

Abstract

Additional file 1: Table S1. Relationships between the 7 components of the PSQI and glycemic variability parameters in patients with T2D.

Published
2021
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25. [Application of the LUR Model in the Prediction of Spatial Distributions of Soil Heavy Metals].

Author

Zeng JJ, Shen CZ, Zhou SL, Lu CF, Jin ZF, and Zhu Y

Abstract

Using the Jintan District of Changzhou City, Jiangsu Province as an example, the LUR model was used to study the spatial distribution of heavy metals and to simulate the spatial distribution of heavy metals in the study area. Compared with the traditional LUR model and the ordinary Kriging interpolation model, the following conclusions were obtained. ① The soil heavy metal content in the study area was highly and significantly correlated with land factors, with the main factor of land use and influencing factors of heavy metals in the soil environment ( P <0.01). In terms of influencing factors, the soil Cu and Zn contents were significantly correlated with the area related to traffic in a 2000 m buffer area and 2000 m buffer zone, respectively. The soil Cr, Cu, and Zn contents were significantly correlated with OM, C org , TC, and TN ( P <0.01). ② The R 2 of the LUR-S models of the spatial distribution of the heavy metals, Pb, Cr, Cu, and Zn, in the study area were improved by 0.041, 0.406, 0.102, and 0.501, respectively, compared with the traditional LUR model. The accuracy test R 2 values were improved by 0.1477, 0.0116, 0.2310, and 0.081, respectively; and the RMSE was reduced by 2.413, 0.631, 1.112, and 2.138, respectively. It was shown that the LUR-S model, which considered the source-sink relationship, had a higher accuracy than the traditional LUR model and ordinary Kriging interpolation model. ③ The LUR-S model was more suitable for the prediction of the spatial distribution of heavy metals with lower pollution and smaller variations, while results for the prediction of the heavy metals with higher pollution and larger variations were worse.

Published
2018
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